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access icon free Fast single shot multibox detector and its application on vehicle counting system

© The Institution of Engineering and Technology
Received 07/02/2018, Accepted 09/08/2018, Revised 02/07/2018, Published 07/09/2018

Real-time vehicle detection and counting of multiple types is a difficult problem. To solve this problem, this study presents an efficient method based on single shot detection (SSD) to construct a vehicle detection and counting system. The proposed method named Fast-SSD first combines the Slim ResNet-34 with Single Shot MultiBox Detector. Then the authors limit the location prediction at each cell in the feature map and modify the detection network. When the input size of the picture is 300 × 300, Fast-SSD achieves the accuracy of 76.7 mAP on the PASCAL visual object classes 2007 test set. The network can be implemented at the speed of 20.8 FPS based on the GTX650Ti. Furthermore, they obtain the centre point of each type of vehicle which is detected by the Fast-SSD model in the image and set the virtual loop detectors to specify the detection range. The number of vehicles is calculated when the centre of the vehicle passes the virtual loop detector. Results show that the vehicle detection accuracy achieves 99.3% and the classification accuracy is 98.9%.

Inspec keywords: image classification; feature extraction; visual databases; object detection; road vehicles

Other keywords: feature map and; GTX650Ti; vehicle counting system; real-time vehicle detection; Fast-SSD model; fast single shot multibox detector; virtual loop detectors; Slim ResNet-34; detection range specification; single shot detection; PASCAL visual object classes 2007 test

Subjects: Computer vision and image processing techniques; Traffic engineering computing; Image recognition

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